Description
This pretrained pipeline is built on the top of ner_oncology_anatomy_general model.
Predicted Entities
How to use
from sparknlp.pretrained import PretrainedPipeline
pipeline = PretrainedPipeline("ner_oncology_anatomy_general_pipeline", "en", "clinical/models")
text = '''The patient presented a mass in her left breast, and a possible metastasis in her lungs and in her liver.'''
result = pipeline.fullAnnotate(text)
import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
val pipeline = new PretrainedPipeline("ner_oncology_anatomy_general_pipeline", "en", "clinical/models")
val text = "The patient presented a mass in her left breast, and a possible metastasis in her lungs and in her liver."
val result = pipeline.fullAnnotate(text)
Results
| | ner_chunks | begin | end | ner_label | confidence |
|---:|:-------------|--------:|------:|:----------------|-------------:|
| 0 | left | 36 | 39 | Direction | 0.9825 |
| 1 | breast | 41 | 46 | Anatomical_Site | 0.9005 |
| 2 | lungs | 82 | 86 | Anatomical_Site | 0.9735 |
| 3 | liver | 99 | 103 | Anatomical_Site | 0.9817 |
Model Information
| Model Name: | ner_oncology_anatomy_general_pipeline |
| Type: | pipeline |
| Compatibility: | Healthcare NLP 4.4.4+ |
| License: | Licensed |
| Edition: | Official |
| Language: | en |
| Size: | 1.7 GB |
Included Models
- DocumentAssembler
- SentenceDetectorDLModel
- TokenizerModel
- WordEmbeddingsModel
- MedicalNerModel
- NerConverterInternalModel